Experimental Setup   
In Figure REF , we illustrate the computational challenges of learning large scale TSP by comparing three identical models trained on 12.8 Million TSP instances via reinforcement learning.
Our experimental setup largely follows Section REF .
All models use identical configurations: autoregressive decoding and Graph ConvNet encoder with Max aggregation and LayerNorm.
The TSP20-50 model is trained using the greedy rollout baseline [1]} and the Adam optimizer with batch size 128 and learning rate \(1e-4\) .
Direct training, active search and finetuning on TSP200 samples is done using learning rate \(1e-5\) , as we found larger learning rates to be unstable.
During active search and finetuning, we use an exponential moving average baseline, as recommended by Bello et al. [2]}.
